However, the losses that such models use to generate realistic images compete with the disentanglement regularization loss [1]}
and the latent representations resulting from VAEs and GANs (disentangled or not) have significantly worse performance on downstream tasks such as retrieval and transfer learning [2]}, [3]}. We therefore consider disentanglement through contrastive representation learning [4]}, [5]}, [6]}, with the hope of overcoming these limitations. Self-supervised contrastive learning frameworks achieve state-of-the-art performance for many tasks such as classification, detection and segmentation [4]}, [8]}, [9]}, so a disentangled contrastive representation that maintains task performance would be of great interest.
